{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "#%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# 颜色\n",
    "color = sns.color_palette()\n",
    "# 数据print精度\n",
    "pd.set_option('precision',3) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 介绍\n",
    "这个notebook分析了红酒的通用数据集。这个数据集有1599个样本，11个红酒的理化性质，以及红酒的品质（评分从0到10）。这里主要目的在于展示进行数据分析的常见python包的调用，以及数据可视化。主要内容分为：单变量，双变量，和多变量分析。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.998</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.997</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.997</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.998</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.998</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0    0.998  3.51       0.56   \n",
       "1                 25.0                  67.0    0.997  3.20       0.68   \n",
       "2                 15.0                  54.0    0.997  3.26       0.65   \n",
       "3                 17.0                  60.0    0.998  3.16       0.58   \n",
       "4                 11.0                  34.0    0.998  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/winequality-red.csv', sep=';')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看数据集基本信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1599 entries, 0 to 1598\n",
      "Data columns (total 12 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   fixed acidity         1599 non-null   float64\n",
      " 1   volatile acidity      1599 non-null   float64\n",
      " 2   citric acid           1599 non-null   float64\n",
      " 3   residual sugar        1599 non-null   float64\n",
      " 4   chlorides             1599 non-null   float64\n",
      " 5   free sulfur dioxide   1599 non-null   float64\n",
      " 6   total sulfur dioxide  1599 non-null   float64\n",
      " 7   density               1599 non-null   float64\n",
      " 8   pH                    1599 non-null   float64\n",
      " 9   sulphates             1599 non-null   float64\n",
      " 10  alcohol               1599 non-null   float64\n",
      " 11  quality               1599 non-null   int64  \n",
      "dtypes: float64(11), int64(1)\n",
      "memory usage: 150.0 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上面分析可看出\n",
    "- 红酒数据集的前10列是特征/属性（有10个），标签是红酒的品质 quality（离散值，位于0-10）。\n",
    "- 特征无缺失值，且数据类型均为float64"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 单变量分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看整体的统计信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1599.000</td>\n",
       "      <td>1599.000</td>\n",
       "      <td>1599.000</td>\n",
       "      <td>1599.000</td>\n",
       "      <td>1599.000</td>\n",
       "      <td>1599.000</td>\n",
       "      <td>1599.000</td>\n",
       "      <td>1599.000</td>\n",
       "      <td>1599.000</td>\n",
       "      <td>1599.000</td>\n",
       "      <td>1599.000</td>\n",
       "      <td>1599.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.320</td>\n",
       "      <td>0.528</td>\n",
       "      <td>0.271</td>\n",
       "      <td>2.539</td>\n",
       "      <td>0.087</td>\n",
       "      <td>15.875</td>\n",
       "      <td>46.468</td>\n",
       "      <td>0.997</td>\n",
       "      <td>3.311</td>\n",
       "      <td>0.658</td>\n",
       "      <td>10.423</td>\n",
       "      <td>5.636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.741</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.195</td>\n",
       "      <td>1.410</td>\n",
       "      <td>0.047</td>\n",
       "      <td>10.460</td>\n",
       "      <td>32.895</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.170</td>\n",
       "      <td>1.066</td>\n",
       "      <td>0.808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.600</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.012</td>\n",
       "      <td>1.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>0.990</td>\n",
       "      <td>2.740</td>\n",
       "      <td>0.330</td>\n",
       "      <td>8.400</td>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.100</td>\n",
       "      <td>0.390</td>\n",
       "      <td>0.090</td>\n",
       "      <td>1.900</td>\n",
       "      <td>0.070</td>\n",
       "      <td>7.000</td>\n",
       "      <td>22.000</td>\n",
       "      <td>0.996</td>\n",
       "      <td>3.210</td>\n",
       "      <td>0.550</td>\n",
       "      <td>9.500</td>\n",
       "      <td>5.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.900</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.260</td>\n",
       "      <td>2.200</td>\n",
       "      <td>0.079</td>\n",
       "      <td>14.000</td>\n",
       "      <td>38.000</td>\n",
       "      <td>0.997</td>\n",
       "      <td>3.310</td>\n",
       "      <td>0.620</td>\n",
       "      <td>10.200</td>\n",
       "      <td>6.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.200</td>\n",
       "      <td>0.640</td>\n",
       "      <td>0.420</td>\n",
       "      <td>2.600</td>\n",
       "      <td>0.090</td>\n",
       "      <td>21.000</td>\n",
       "      <td>62.000</td>\n",
       "      <td>0.998</td>\n",
       "      <td>3.400</td>\n",
       "      <td>0.730</td>\n",
       "      <td>11.100</td>\n",
       "      <td>6.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.900</td>\n",
       "      <td>1.580</td>\n",
       "      <td>1.000</td>\n",
       "      <td>15.500</td>\n",
       "      <td>0.611</td>\n",
       "      <td>72.000</td>\n",
       "      <td>289.000</td>\n",
       "      <td>1.004</td>\n",
       "      <td>4.010</td>\n",
       "      <td>2.000</td>\n",
       "      <td>14.900</td>\n",
       "      <td>8.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "count       1599.000          1599.000     1599.000        1599.000   \n",
       "mean           8.320             0.528        0.271           2.539   \n",
       "std            1.741             0.179        0.195           1.410   \n",
       "min            4.600             0.120        0.000           0.900   \n",
       "25%            7.100             0.390        0.090           1.900   \n",
       "50%            7.900             0.520        0.260           2.200   \n",
       "75%            9.200             0.640        0.420           2.600   \n",
       "max           15.900             1.580        1.000          15.500   \n",
       "\n",
       "       chlorides  free sulfur dioxide  total sulfur dioxide   density  \\\n",
       "count   1599.000             1599.000              1599.000  1599.000   \n",
       "mean       0.087               15.875                46.468     0.997   \n",
       "std        0.047               10.460                32.895     0.002   \n",
       "min        0.012                1.000                 6.000     0.990   \n",
       "25%        0.070                7.000                22.000     0.996   \n",
       "50%        0.079               14.000                38.000     0.997   \n",
       "75%        0.090               21.000                62.000     0.998   \n",
       "max        0.611               72.000               289.000     1.004   \n",
       "\n",
       "             pH  sulphates   alcohol   quality  \n",
       "count  1599.000   1599.000  1599.000  1599.000  \n",
       "mean      3.311      0.658    10.423     5.636  \n",
       "std       0.154      0.170     1.066     0.808  \n",
       "min       2.740      0.330     8.400     3.000  \n",
       "25%       3.210      0.550     9.500     5.000  \n",
       "50%       3.310      0.620    10.200     6.000  \n",
       "75%       3.400      0.730    11.100     6.000  \n",
       "max       4.010      2.000    14.900     8.000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义画布的风格 \n",
    "# plt.style.available  # 自带风格\n",
    "plt.style.use('ggplot')  # "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "绘制各单边量的箱型图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Figure 1: Univariate Boxplots\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colnm = df.columns.tolist()\n",
    "fig = plt.figure(figsize = (10, 6))\n",
    "\n",
    "for i in range(12):\n",
    "    plt.subplot(2, 6,i+1)  # 准备2*6的子画布 刚好绘制12个单变量的箱型图\n",
    "    sns.boxplot(df[colnm[i]], orient=\"v\", width = 0.5, color = color[0])  # 绘制各单变量（Dataframe各列）的箱型图\n",
    "    plt.ylabel(colnm[i],fontsize = 12)\n",
    "#plt.subplots_adjust(left=0.2, wspace=0.8, top=0.9)\n",
    "\n",
    "plt.tight_layout()\n",
    "print('\\nFigure 1: Univariate Boxplots')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
